{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e52dc391",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "952acacc",
   "metadata": {},
   "source": [
    "**abc**"
   ]
  },
  {
   "cell_type": "raw",
   "id": "3a7954d1",
   "metadata": {},
   "source": [
    "for i in range(10):\n",
    "   i += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ee5ab6",
   "metadata": {},
   "source": [
    "# 第一段"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d882af45",
   "metadata": {},
   "source": [
    "这是一个课程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fbf41c95",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "data = np.arange(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5ad1c967",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tab键补全"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2f0fc15d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# shift + 两次tab显示代码文档\n",
    "np.random.randn()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45887cfc",
   "metadata": {},
   "source": [
    "## 魔术命令"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c1ce418d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "946bdcfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e45cc434c8>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cbede34b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Vincent\\\\wor\\\\tensorflow-stu'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 当前目录\n",
    "# 魔术命令pycharm不识别\n",
    "# jupyter notebook可以远程访问服务器\n",
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f016a08e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "497 µs ± 26.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit [x**3 for x in range(1000)]"
   ]
  },
  {
   "cell_type": "raw",
   "id": "b203a63a",
   "metadata": {},
   "source": [
    "# 远程访问\n",
    "jupyter notebook --no-browser --port=8889 --ip=0.0.0.0 --allow-root"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c95bebc",
   "metadata": {},
   "source": [
    "# 歇会"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1a72a50",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
